Ethical Issues Raised by Private Practice Physiotherapy Are More Diverse than First Meets the Eye: Recommendations from a Literature Review
Bibliographic record
Abstract
PURPOSE: Physiotherapy in private practice differs from physiotherapy practised in a public setting in several ways, the most evident of which is the for-profit nature of private physiotherapy clinics; these differences can generate distinct and challenging ethical issues. The objectives of this article are to identify ethical issues encountered by physiotherapists in private practice settings and to identify potential solutions and recommendations to address these issues. METHOD: After a literature search of eight databases, 39 studies addressing ethical issues in a private practice context were analyzed. RESULTS: A total of 25 ethical issues emerging from the included studies were classified into three main categories: (1) business and economic issues (e.g., conflicts of interests, inequity in a managed care context, lack of time affecting quality of care); (2) professional issues (e.g., professional autonomy, clinical judgment, treatment effectiveness, professional conduct); and (3) patients' rights and welfare issues (e.g., confidentiality, power asymmetries, paternalism vs. patient autonomy, informed consent). Recommendations as to how physiotherapists could better manage these issues were then identified and categorized. CONCLUSIONS: The physiotherapy community should reflect on the challenges raised by private practice so that professionals can be supported-through education, research, and good governance-in providing the best possible care for their patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".